2 Dec 2022| OES
Data Science & Big Data
Apache Spark: Efficient Source To Process Big Data
Apache spark is renowned for its fast, flexible and developer-friendly environment. The platform turned out to be the best leading platform for extensive SQL, stream processing, batch processing and machine learning.
Apache spark executes data processing through a framework whose performance levels are very rapid and highly effective in dealing with large data sets. It can also distribute data processing tasks across multiple devices.
Such interesting and unique features are vital for finding big data and machine learning paths. Also, Spark eases the task of the developers by providing an API that is easy to use.
Spark was initiated in 2009. From then on, it developed into one of the significant data distributed processing frameworks globally. Varied ways exist to deploy Spark, and its usage is widespread across various banks, government, games and telecommunication companies. All the major tech giants, including Google, Apple and IBM, use Spark efficiently.
Having gained an idea about Apache spark, let us get ahead to know about Apache architecture:
The two main components of Apache Spark include the Drivers and Executors. Driver plays a crucial role in converting the user’s code into several tasks to be sent across multiple nodes. The role of the executors is to run on these nodes and implement tasks. Significantly, there is necessary to mediate between the two through some cluster manager.
Unusually good, Spark can function in a stand-alone application. And, the requirements include the Apache spark framework, and JVM installed on each machine. What are the essentials it can run on ? Apache Mesos, Kubernetes and Docker Swarm are the ones that hold significance.
With such a unique and effective set of features, let us know how to process big data:
Hadoop has been in the field of big data processing technology for the past ten years. The system has developed the capacity to handle large data sets. The effective solution for one-pass computation lies with MapReduce. However, it is not found effective for the use cases requiring multi-pass computation.
Every step of the data processing workflow contains the Map phase and Reduce phase, and the necessity lies to convert any use case into a MapReduce pattern. Further action is the output generated at each step should find storage in distributed file system prior to the next step. This process results in slow execution and high data storage.
At the same time, the Hadoop solutions, including clusters, are also tough to manage. Going further, if the necessity lies to perform a more complicated system, the MapReduce jobs have to perform in sequence one after the other. Moreover, it requires the integration of various tools for different uses cases.
Spark functioning is more superior. The functioning of the system is more enhanced with added functionality. The significant features include the following :
Apache spark benefits are enormous. Though more complexities set in, the spark systems are capable enough to deal with efficiently compared to Hadoop. However, spark systems have to gain more maturity in security and BI tool integration.
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